Multidimensional flow cytometry allows the discrimination of neoplastic cells from their normal counterparts. Using technology for the simultaneous measurement of 6 parameters, the abnormal expression of normal antigens can be used to identify leukemic populations. Distinction between normal and abnormal cells can be made by a highly trained scientist using a computer program which permits visualization of multidimensional data. This proposal will demonstrate the utility of a software program to reproduce the identification made by a trained scientist. We will focus on antigen expression on B lymphoid cells in bone marrow specimens that exhibit normal phenotypes. The software will follow the biological rules established by the trained scientist to identify the locations of normal cells in multidimensional space. Using this technology we will characterize acute lymphoblastic leukemias and report their relative positions in a multidimensional data space. We will mimic residual disease detection by combining normal and abnormal data to assess the value of the software technology in facilitating the identification of low levels of tumor cells in a background of normal cells.